In the field of digital medical imaging, particularly computed radiography, photostimulable phosphors are used for image acquisition. The dynamic range of these devices is linear over a 10,000:1 range as compared to a 40:1 range for x-ray film. Because of this huge range of detectable exposures, the necessity of re-imaging a patient due to improper selection of exposure factors is virtually eliminated. However, with the decoupling of the acquisition media from the display device, it is necessary to develop a mapping function, often in the form of a look-up-table (LUT), which will optimally render the image on the desired output medium (CRT display, film, reflection print).
In developing the optimal tone scale, it has been observed that several key factors contribute to the acceptability of the resulting image. First, it is desirable to present the direct x-ray exposure regions as black as possible. Second, it is necessary to preserve the perceptibility of the skin-line in various exams, such as extremities. In order to be successful at meeting these requirements, one needs to identify in what code-value range the background pixels reside. Having done this, a tone scale algorithm can then make the necessary adjustments to ensure that both criteria are met.
The problem of background detection falls into the domain of medical image segmentation. Two general methodologies exist; 1) histogram analysis, and 2) spatial/texture analysis (possibly combined with gray level intensity information provided by the histogram). Of the first class of methods, U.S. Pat. No. 5,046,118, inventors Ajewole, et al., discloses a method which uses the concept of partial entropy to divide the histogram into a background region and a non-background region. U.S. Pat. No. 5,164,993, inventors Capozzi et al., refers to the latter method and uses it on both the linear and logarithmic histograms, with some additional provisions for conditions when the background point is found at the top of a peak. EP Patent Appln. 288,042, inventors Tanaka et al., discloses a method for finding background and foreground (which are areas of an image that have received very little radiation due to the use of radiation limiting devices such as collimator blades) using a histogram divided into a number of sections by an automatic thresholding procedure. In Tanaka, a discriminant analysis, combined with information about the exam type, exposure technique and desired body portion to be displayed, is then used to adjust the separation points between the sections until the desired ranges for the foreground, object, and background regions are found.
As part of the second group of methodologies, U.S. Pat. No. 5,268,967, inventors Jang et al., discloses a four step method which involves morphological edge detection, block generation, block classification, and block refinement. Segmentation via texture analysis using well known texture features and a neural network classifier is disclosed in Barski et al., "A Neural Network Approach to the Histogram Segmentation of Digital Radiographic Images", ANNIE '93 Conference Proceedings.
The methods disclosed in Ajewole, Capozzi, and Tanaka, above, may not be useful when the background is varying in a nonuniform way or when multiple background peaks exist in the histogram of the image. The method disclosed in Jang is useful in certain applications but complex processing stages are involved which are time consuming. The texture analysis method is very slow when running in a software implementation and only modestly reliable.